Our goal is to develop and commercialize live cell, time-lapse microscopy image informatics software specialized for the high throughput quantification of subcellular functions such as the analysis of signaling in spines and dendrites. The software integrates novel and robust methods of subcellular time-lapse analysis and modeling not available in the current informatics tools for the enhancement of signal detection and noise immunity to improve on assay throughput, accuracy, efficiency and reliability. The general informatics framework of a quantitative time-lapse assay consists of (1) the model, (2) the data, and (3) the fitting of data to the model. We will create three levels of robust kinetic analysis algorithm innovations to enhance the data quality, model, as well as model fitting accuracy and efficiency. Our specific aims are 1) Complete the development of the robust object detection algorithm for time-lapse subcellular assay images and quantitatively validate its efficacy; 2) Complete the development of robust timelapse signal enhancement and feature measurement algorithms and quantitatively validate its efficacy; and 3) Initial development of assay outcome-directed model fitting and auto-correction algorithms, and quantitatively assess the improvement in assay quality. To establish the feasibility of the approach, we have selected the image based synaptic vesicle recycling assay as our target assay. It is an important indicator of neuronal function, is characterized by weak and unstable signal and is particularly sensitive to noise, has a strong requirement for automatic and quantitative analysis, provides a compelling benefit over alternative assay approaches (electrophysiology or electron microscopy), and is beneficial for both academic research and drug discovery screening. At the end of the phase I, we will have produced a working prototype for the robust analysis of synaptic vesicle recycling assays. We expect to validate the feasibility of our innovations by significantly improved assay quality using Z-factor and S:N. This informatics software can play a key role in speeding the translation of lab discoveries into high throughput screens for drug discovery.